Data loading and cohort selection

Loading

afs_orig = read.csv("data/MGRB_GnomAD_SweGen_cancer_UKBB_AFs_outerjoined_ss-auto-gwas-snps_hcr.csv.xz", stringsAsFactors = FALSE, header = TRUE)
models_orig = read.csv("data/manual_polygenic_scores.hcr_tag_rescued.csv", stringsAsFactors = FALSE, header = TRUE)

# Key allele frequencies by VID
afs_orig$vid = paste(afs_orig$chrom, afs_orig$pos, afs_orig$ref, afs_orig$alt, sep = ":")
afs_orig = afs_orig[,!(colnames(afs_orig) %in% c("chrom", "pos", "ref", "alt"))]

# Add gnomad AFs to the models for imputation of missing variants
temp.gnomad_af = (afs_orig$nAA_gnomad*2 + afs_orig$nRA_gnomad) / (2*(afs_orig$nAA_gnomad + afs_orig$nRA_gnomad + afs_orig$nRR_gnomad))
models_orig$aaf = temp.gnomad_af[match(models_orig$vid, afs_orig$vid)]

# Create a full UKBB cohort by combining the age-stratified numbers
afs_orig$nRR_ukbb = afs_orig$nRR_ukbb_0_55 + afs_orig$nRR_ukbb_55_60 + afs_orig$nRR_ukbb_60_65 + afs_orig$nRR_ukbb_65_70 + afs_orig$nRR_ukbb_70_75 + afs_orig$nRR_ukbb_75_inf
afs_orig$nRA_ukbb = afs_orig$nRA_ukbb_0_55 + afs_orig$nRA_ukbb_55_60 + afs_orig$nRA_ukbb_60_65 + afs_orig$nRA_ukbb_65_70 + afs_orig$nRA_ukbb_70_75 + afs_orig$nRA_ukbb_75_inf
afs_orig$nAA_ukbb = afs_orig$nAA_ukbb_0_55 + afs_orig$nAA_ukbb_55_60 + afs_orig$nAA_ukbb_60_65 + afs_orig$nAA_ukbb_65_70 + afs_orig$nAA_ukbb_70_75 + afs_orig$nAA_ukbb_75_inf
afs_orig$nmissing_ukbb = afs_orig$nmissing_ukbb_0_55 + afs_orig$nmissing_ukbb_55_60 + afs_orig$nmissing_ukbb_60_65 + afs_orig$nmissing_ukbb_65_70 + afs_orig$nmissing_ukbb_70_75 + afs_orig$nmissing_ukbb_75_inf

# Convert afs from wide to long format
library(reshape2)
afs_long = melt(afs_orig, id.vars = c("rsid", "negstrand", "vid"), value.name = "count")
afs_long$cohort = gsub("^n(RR|RA|AA|missing)_", "", afs_long$variable)
afs_long$variable = gsub("_.*", "", afs_long$variable)
afs = dcast(afs_long, vid + rsid + negstrand + cohort ~ variable, value.var = "count")
afs = afs[,c("vid", "rsid", "negstrand", "cohort", "nRR", "nRA", "nAA", "nmissing")]

# Exclude ASRB samples -- prelim examination suggests they are rather
# poor quality, and we are not interested in their PRS distributions
# anyway.  Also exclude the various MGRB filtration options, as they
# apply only to rare variants.  Exclude SweGen as we don't have a good
# HQ bed for it.
cohorts.sel = c(
    "mgrborig", 
    "gnomad", 
    "ukbb", "ukbb_0_55", "ukbb_55_60", "ukbb_60_65", "ukbb_65_70", "ukbb_70_75", "ukbb_75_inf",
    "cancercrc", "cancermel", "cancernms", "cancerbrca_f", "cancerpca_m",
    "nocancer", "nocancer_f", "nocancer_m", "mgrborig_f", "mgrborig_m")
cohorts.main = c("mgrborig", "gnomad", "ukbb")
afs = afs[afs$cohort %in% cohorts.sel,]

Model selection

Choose polygenic models with at least 10 loci, with the exception of ShortLifespan:Deelen:10.1093/hmg/ddu139 (only six loci passing filters). For cancers, choose polygenic models only for cancers with a positive control cohort. In the case of multiple models for the same disorder, choose the most recent original publication where possible (ie exclude “meta” signatures if a good original report is available).

# Excluded due to insufficient size:
    # "CancerOfBladder:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "LymphoidLeukemiaAcute:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "LymphoidLeukemiaChronic:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "MalignantNeoplasmOfTestis:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "NonHodgkinsLymphoma:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "PancreaticCancer:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "APOE_rs429358:NA:NA",
# Excluded because a better alternative was available
    # "CancerOfProstate:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "ColorectalCancer:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "MelanomasOfSkin:Fritsche:10.1016/j.ajhg.2018.04.001",
    # "BreastCancer:Li:10.1038/gim.2016.43",
    # "BreastCancerFemale:Fritsche:10.1016/j.ajhg.2018.04.001",
# Excluded because of issues with population-specific alleles between UK and European popns
    # "BasalCellCarcinoma:Chahal:10.1038/ncomms12510",
    # "BasalCellCarcinoma:Fritsche:10.1016/j.ajhg.2018.04.001",
models.sel = c(
    "AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143",
    "DiastolicBP:Warren:10.1038/ng.3768",
    "EOCAD:Theriault:10.1161/circgen.117.001849",
    "PulsePressure:Warren:10.1038/ng.3768",
    "SystolicBP:Warren:10.1038/ng.3768",

    "AlzheimersDisease:Lambert:10.1038/ng.2802",
    "ShortLifespan:Deelen:10.1093/hmg/ddu139",

    "Height:Wood:10.1038/ng.3097",

    "BreastCancer:Michailidou:10.1038/nature24284",
    "ColorectalCancer:Schumacher:10.1038/ncomms8138",
    "Melanoma:Law:10.1038/ng.3373",
    "ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315"
)
models = models_orig[models_orig$id %in% models.sel,]

Variant filtering and imputation

# Drop variants with low genotyping rate in any cohort in which that
# variant was detected.  Use the threshold of 97% genotyping rate
library(plyr)
temp.lowgt = ddply(afs[!is.na(afs$nRR),], .(cohort), function(d) mean(d$nmissing / (d$nRR + d$nRA + d$nAA + d$nmissing) >= 0.03))
temp.lowgt
##          cohort           V1
## 1  cancerbrca_f 1.637465e-04
## 2     cancercrc 1.228099e-04
## 3     cancermel 1.228099e-04
## 4     cancernms 8.187326e-05
## 5   cancerpca_m 4.093663e-05
## 6        gnomad 1.598146e-03
## 7      mgrborig 8.074609e-05
## 8    mgrborig_f 8.074609e-05
## 9    mgrborig_m 8.074609e-05
## 10     nocancer 8.187326e-05
## 11   nocancer_f 4.093663e-05
## 12   nocancer_m 1.228099e-04
## 13         ukbb 5.066030e-02
## 14    ukbb_0_55 5.120115e-02
## 15   ukbb_55_60 5.097580e-02
## 16   ukbb_60_65 5.097580e-02
## 17   ukbb_65_70 5.057015e-02
## 18   ukbb_70_75 5.038987e-02
## 19  ukbb_75_inf 4.989408e-02
# The UKBB samples have rather a lot of dropouts here: ~ 5% of loci
# have a gt rate under 97%.

temp.gt_rate = 1 - daply(afs[!is.na(afs$nRR),], .(vid), function(d) max(d$nmissing / (d$nRR + d$nRA + d$nAA + d$nmissing)))
mean(temp.gt_rate < 0.97)
## [1] 0.04893795
afs = afs[afs$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97],]
mean(models$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97])
## [1] 0.9484241
# ~5.2% of model loci lost by this filter
models = models[models$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97],]

# Create a set of AFs for variants that have VCF entries in every cohort.  
# Note that given the relatively small size of some cohorts, this tends to 
# preferentially exclude rare variants from consideration, and will probably 
# attenuate the GRS differences.
afs.nmissing_per_cohort = tapply(is.na(afs$nRR), afs$vid, sum)
afs.nomissing = afs[!(afs$vid %in% names(afs.nmissing_per_cohort[afs.nmissing_per_cohort > 0])),]
nrow(afs.nomissing) / nrow(afs)
## [1] 0.8663734
mean(models$vid %in% afs.nomissing$vid)
## [1] 0.6628399

Cohort AF comparisons

Note that we examine only loci in the PRSes.

Overall distribution

temp.model_vids = intersect(afs.nomissing$vid, models$vid)
temp.afs = afs.nomissing[afs.nomissing$vid %in% temp.model_vids,]
temp.afs$fA = (temp.afs$nAA*2 + temp.afs$nRA) / (2*(temp.afs$nAA + temp.afs$nRA + temp.afs$nRR))
temp.afs = acast(temp.afs, vid ~ cohort, value.var = "fA", fill = NA)
pairs(temp.afs[,cohorts.main], pch = ".")

temp.overall_cohort.pvals = sapply(cohorts.main[1:(length(cohorts.main)-1)], function(cohort_1) {
    cohort_1_idx = which(cohorts.main == cohort_1)
    afs_1 = temp.afs[,cohort_1]
    sapply(cohorts.main[(cohort_1_idx+1):length(cohorts.main)], function(cohort_2) {
        afs_2 = temp.afs[,cohort_2]
        test = wilcox.test(afs_1 - afs_2)
        test$p.value
    })
})

temp.overall_cohort.pvals
## $mgrborig
##     gnomad       ukbb 
## 0.69792760 0.01866417 
## 
## $gnomad
##      ukbb 
## 0.3782686
p.adjust(unlist(temp.overall_cohort.pvals), "holm")
## mgrborig.gnomad   mgrborig.ukbb     gnomad.ukbb 
##      0.75653715      0.05599252      0.75653715

Individual loci

g.test = function(tbl)
{
    expected = outer(rowSums(tbl), colSums(tbl)) / sum(tbl)
    logoe = log(tbl/expected)
    logoe[tbl == 0] = 0
    stat = 2*sum(tbl*logoe)
    pchisq(stat, prod(dim(tbl)-1), lower.tail = FALSE)
}

temp.locus_cohort.pvals = ddply(afs.nomissing[afs.nomissing$vid %in% temp.model_vids & afs.nomissing$cohort %in% cohorts.main,], .(vid), function(d) {
    nR = d$nRR*2 + d$nRA
    nA = d$nAA*2 + d$nRA
    g.test(cbind(nR, nA))
    # g.test(as.matrix(d[,c("nRR", "nRA", "nAA")]))
})
colnames(temp.locus_cohort.pvals)[2] = "p.value"
temp.locus_cohort.pvals$p.value.mtc = p.adjust(temp.locus_cohort.pvals$p.value, "BH")
# temp.locus_cohort.pvals$p.value.mtc is now calibrated for average false rejection rate 
# (ie calling a SNP population-associated when it in fact isn't).

mean(temp.locus_cohort.pvals$p.value.mtc < 0.01)
## [1] 0.4721461
temp.locus_cohort.maxdeltaaf = ddply(afs.nomissing[afs.nomissing$vid %in% temp.model_vids & afs.nomissing$cohort %in% cohorts.main,], .(vid), function(d) {
    nR = d$nRR*2 + d$nRA
    nA = d$nAA*2 + d$nRA
    AAF = nA/(nA+nR)
    max(AAF) - min(AAF)
})
colnames(temp.locus_cohort.maxdeltaaf)[2] = "maxdeltaaaf"

temp.locus_cohort.maxdeltaaf[order(temp.locus_cohort.maxdeltaaf$maxdeltaaaf),]
##                   vid  maxdeltaaaf
## 629   20:41851935:G:A 4.804901e-05
## 175     11:244552:A:G 5.472387e-04
## 680   3:163838015:A:C 5.854864e-04
## 832   5:176517326:T:C 6.520533e-04
## 849    5:58337481:T:G 6.896540e-04
## 576   2:232268312:T:C 8.648204e-04
## 64    1:242034263:A:G 8.926576e-04
## 557    2:19942473:G:A 8.944422e-04
## 783    4:61995613:A:G 9.804085e-04
## 343   15:32993111:C:T 1.013646e-03
## 125   10:12943973:C:T 1.036166e-03
## 428   17:43216281:C:T 1.120399e-03
## 923    6:43711981:T:C 1.242711e-03
## 87     1:65010606:T:G 1.410640e-03
## 1076   9:22062134:G:T 1.487932e-03
## 943    6:81792063:G:T 1.508864e-03
## 368   15:72842705:G:A 1.511067e-03
## 716    3:62133492:G:A 1.517305e-03
## 754   4:147993702:A:G 1.550150e-03
## 49    1:215046892:G:A 1.631525e-03
## 45    1:203766331:A:G 1.651584e-03
## 775    4:39503196:A:G 1.678110e-03
## 799   5:108625324:C:A 1.746323e-03
## 34     1:17308254:T:C 1.809376e-03
## 462   18:19450303:A:G 1.867013e-03
## 460     17:800593:T:C 1.957503e-03
## 661   21:45867411:G:A 1.968592e-03
## 290   13:55934157:A:G 1.984521e-03
## 1095   9:99203606:T:C 2.015219e-03
## 543   2:179786068:T:C 2.214285e-03
## 221  12:125441159:T:C 2.246113e-03
## 364   15:67455630:C:T 2.312045e-03
## 444   17:64783539:C:T 2.316572e-03
## 1064  9:118826916:G:A 2.361107e-03
## 383   15:98615560:C:T 2.370182e-03
## 698   3:190815978:A:G 2.376253e-03
## 17    1:151259043:C:T 2.419978e-03
## 950   7:107259721:T:C 2.424953e-03
## 918    6:36659932:C:T 2.450101e-03
## 670   3:136107549:G:A 2.505036e-03
## 13     1:14105298:G:A 2.546910e-03
## 871   6:118569679:T:G 2.552647e-03
## 993   8:110115372:C:T 2.598390e-03
## 158   11:11563879:C:T 2.615930e-03
## 300   13:80618435:A:G 2.616690e-03
## 965   7:148629759:C:T 2.621543e-03
## 701    3:27416013:C:T 2.622335e-03
## 548   2:191227755:A:G 2.638522e-03
## 477   18:57323149:C:A 2.650551e-03
## 1005  8:128106880:A:C 2.808417e-03
## 36    1:177279412:G:A 2.814433e-03
## 791    4:82204091:A:G 2.816117e-03
## 936    6:75452066:T:C 2.865091e-03
## 502   19:42683964:C:T 2.865981e-03
## 801   5:113748571:C:T 2.886389e-03
## 633   20:50141264:T:C 2.900362e-03
## 883   6:149608874:G:A 2.955662e-03
## 995   8:115698881:G:A 2.962404e-03
## 482   18:77222862:T:G 2.971526e-03
## 332   14:90636206:G:A 3.008568e-03
## 572   2:219903723:C:T 3.051369e-03
## 650   21:27208935:G:T 3.062235e-03
## 827   5:171285632:C:T 3.075935e-03
## 606    2:56113538:G:A 3.098396e-03
## 812   5:141681788:G:A 3.104595e-03
## 1067  9:127900996:T:C 3.347011e-03
## 687   3:178467852:A:G 3.415386e-03
## 618   20:19465907:G:A 3.418120e-03
## 691   3:185313855:A:G 3.418663e-03
## 153   10:97805074:G:A 3.442529e-03
## 942    6:81038921:T:G 3.447571e-03
## 994   8:110581794:A:G 3.470129e-03
## 180   11:45643843:C:T 3.483223e-03
## 466   18:29088958:C:T 3.498834e-03
## 865   6:109285189:A:G 3.553083e-03
## 390    16:2345388:T:C 3.579194e-03
## 58    1:227191011:A:G 3.623518e-03
## 777    4:42308930:C:T 3.649195e-03
## 600    2:44392271:C:A 3.688753e-03
## 939     6:7709052:G:T 3.701351e-03
## 723    3:89530057:C:A 3.732115e-03
## 739   4:111765495:C:T 3.739535e-03
## 1000  8:123980551:C:T 3.752414e-03
## 1038   8:57095808:T:C 3.768970e-03
## 808   5:134499092:C:A 3.775173e-03
## 790    4:82184049:C:T 3.790315e-03
## 342   15:30167418:G:A 3.798314e-03
## 8      1:11852516:C:T 3.841580e-03
## 674   3:141163045:T:C 3.905120e-03
## 686   3:178343683:C:T 3.911620e-03
## 362   15:66997087:A:G 3.913701e-03
## 96     1:89360520:T:G 3.941459e-03
## 1085   9:92498089:C:T 3.992649e-03
## 437   17:56431549:A:G 4.036653e-03
## 744   4:119121695:C:T 4.041940e-03
## 480   18:74983055:A:G 4.072636e-03
## 164  11:128478885:C:A 4.108551e-03
## 82     1:54119578:G:A 4.159979e-03
## 1037   8:56998480:G:A 4.187366e-03
## 1080    9:6365683:A:C 4.206827e-03
## 433   17:47944836:G:A 4.216069e-03
## 571   2:219154781:G:A 4.227556e-03
## 1066  9:126094366:T:C 4.254321e-03
## 438   17:59483766:C:T 4.254752e-03
## 647   21:16520832:G:A 4.264179e-03
## 212  12:114804580:A:C 4.266542e-03
## 268  13:111100780:C:T 4.279067e-03
## 578   2:232796610:T:C 4.294600e-03
## 315   14:53391680:A:G 4.313766e-03
## 307  14:100599519:T:C 4.321819e-03
## 703    3:38036914:C:T 4.336252e-03
## 848    5:56254485:G:T 4.379536e-03
## 919    6:39134099:T:C 4.392131e-03
## 699   3:191111160:A:G 4.457648e-03
## 73     1:32371442:C:T 4.488190e-03
## 901    6:17589375:T:C 4.528051e-03
## 40    1:195866450:T:G 4.531433e-03
## 440   17:62161933:A:G 4.543676e-03
## 176    11:2810731:C:T 4.564121e-03
## 327   14:70609793:G:A 4.566616e-03
## 309   14:24830850:T:C 4.616386e-03
## 273   13:22453087:T:C 4.668375e-03
## 467   18:29363000:G:A 4.700294e-03
## 337   14:93784292:A:C 4.705838e-03
## 930    6:51450906:C:T 4.742416e-03
## 114  10:114773927:A:G 4.743066e-03
## 831   5:175947118:G:T 4.774634e-03
## 539   2:177557020:A:G 4.794079e-03
## 299   13:78474468:A:C 4.812120e-03
## 43     1:20030836:G:A 4.816278e-03
## 174   11:23197362:G:A 4.825182e-03
## 83     1:56965664:C:T 4.866564e-03
## 510   19:56323209:T:C 4.882986e-03
## 395   16:52599188:C:T 4.899116e-03
## 135   10:44777188:C:A 4.900737e-03
## 839    5:32821168:A:G 4.908845e-03
## 1010  8:129194641:C:T 4.921311e-03
## 134   10:44480811:T:G 4.943897e-03
## 267  13:111049623:T:C 4.952131e-03
## 170    11:2022804:A:G 4.978664e-03
## 414    17:1353920:T:G 5.093687e-03
## 733   4:109408608:G:A 5.102530e-03
## 574   2:221397008:T:C 5.228419e-03
## 426   17:40565926:T:G 5.228988e-03
## 249    12:6385727:C:T 5.238769e-03
## 722    3:74522679:G:A 5.286244e-03
## 523   2:142077308:T:C 5.309499e-03
## 347   15:39531658:A:G 5.312263e-03
## 351   15:51269629:A:G 5.343431e-03
## 384   15:99219598:C:T 5.350541e-03
## 348   15:39597034:T:C 5.358179e-03
## 264   12:96027759:A:G 5.362742e-03
## 999   8:117630683:A:C 5.369172e-03
## 810   5:137044526:T:C 5.376612e-03
## 1059  9:114850255:A:C 5.388091e-03
## 770    4:29571133:C:T 5.413917e-03
## 376   15:81836638:G:A 5.443466e-03
## 90     1:77559860:G:A 5.447211e-03
## 755   4:148400819:C:A 5.449392e-03
## 768   4:184529029:T:G 5.475820e-03
## 132   10:35150975:A:G 5.499058e-03
## 1073  9:139111870:G:T 5.525798e-03
## 152   10:96632253:T:C 5.534003e-03
## 542   2:179349227:T:G 5.541236e-03
## 142   10:75683793:G:A 5.549552e-03
## 835    5:31540303:A:G 5.551595e-03
## 880    6:13722523:G:A 5.632665e-03
## 103  10:102604514:G:A 5.670602e-03
## 341  15:101762539:C:T 5.709396e-03
## 50     1:21584585:A:G 5.722582e-03
## 141   10:75416789:A:G 5.731772e-03
## 210  12:114556438:C:T 5.756619e-03
## 814   5:142928348:C:A 5.757560e-03
## 1069  9:133464084:A:G 5.818366e-03
## 676    3:14928077:G:T 5.820147e-03
## 945    6:82442022:G:A 5.842640e-03
## 429   17:46096276:C:T 5.845469e-03
## 547   2:190404135:C:T 5.887918e-03
## 457    17:7571752:T:G 5.938427e-03
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## 1029   8:27527995:C:T 2.213876e-02
## 637   20:57472174:C:T 2.213893e-02
## 820   5:158244083:C:T 2.214279e-02
## 1057  9:110895353:C:T 2.215313e-02
## 581    2:24247514:A:G 2.215940e-02
## 179   11:45274894:C:T 2.216225e-02
## 813   5:142792484:G:T 2.217652e-02
## 155   11:10657739:C:T 2.223775e-02
## 560   2:203194256:A:C 2.227200e-02
## 655   21:35659776:G:A 2.227678e-02
## 195   11:68994667:A:G 2.228755e-02
## 322   14:63928710:T:C 2.232707e-02
## 252   12:66359752:C:A 2.238603e-02
## 506   19:45422846:G:A 2.243868e-02
## 375   15:81013037:G:A 2.244044e-02
## 846    5:53361606:T:C 2.247655e-02
## 516   2:108643385:A:G 2.258235e-02
## 190   11:65390803:G:A 2.269053e-02
## 163   11:12679245:T:G 2.271505e-02
## 202   11:80644946:T:G 2.273537e-02
## 497   19:31047269:A:G 2.283462e-02
## 1041   8:76040583:C:T 2.293474e-02
## 878    6:12903957:A:G 2.297638e-02
## 501   19:41922352:A:G 2.299547e-02
## 900   6:168834623:C:T 2.301010e-02
## 80     1:49070646:T:C 2.310176e-02
## 492   19:17844894:A:G 2.317985e-02
## 263   12:94880742:G:A 2.318080e-02
## 804   5:127868199:G:A 2.333346e-02
## 369   15:73370467:G:A 2.337196e-02
## 447   17:69108753:G:T 2.340381e-02
## 860    5:88327782:T:G 2.341532e-02
## 771    4:36327093:G:A 2.341975e-02
## 611    2:71584485:G:A 2.342075e-02
## 910    6:32373185:T:C 2.345255e-02
## 549   2:191437024:A:G 2.354694e-02
## 582    2:25040082:C:T 2.365119e-02
## 27    1:160318940:A:G 2.367938e-02
## 973    7:20381674:G:A 2.370621e-02
## 809   5:135678996:T:C 2.372241e-02
## 1     1:103519589:G:A 2.387109e-02
## 914    6:34773727:C:T 2.390860e-02
## 320   14:59688820:G:A 2.392682e-02
## 966   7:150508720:C:T 2.398928e-02
## 1077   9:27982137:A:G 2.412054e-02
## 140   10:70196580:T:C 2.421556e-02
## 28    1:160399586:T:C 2.432670e-02
## 144   10:80819132:A:G 2.432806e-02
## 316   14:54387873:C:T 2.435820e-02
## 445   17:66303352:G:A 2.443567e-02
## 685   3:173179298:C:T 2.458142e-02
## 452   17:73908566:A:C 2.463523e-02
## 1092   9:97713459:C:A 2.473967e-02
## 12     1:13885203:C:T 2.481426e-02
## 272   13:22319948:T:C 2.481499e-02
## 458   17:76718842:C:T 2.498174e-02
## 504   19:44286513:A:G 2.501058e-02
## 499   19:38346685:A:G 2.501788e-02
## 285   13:50469913:C:T 2.505273e-02
## 998   8:117563532:G:A 2.514788e-02
## 302   13:81550449:T:C 2.521085e-02
## 584    2:25463483:G:A 2.525806e-02
## 551   2:191832662:T:C 2.538334e-02
## 679   3:158285175:T:C 2.544736e-02
## 608    2:57218797:G:T 2.552728e-02
## 148   10:82170856:A:G 2.555518e-02
## 453   17:73949045:C:T 2.556254e-02
## 1014  8:135612745:A:G 2.557238e-02
## 19    1:151763246:G:A 2.566957e-02
## 277   13:29022645:C:T 2.567455e-02
## 983    7:46201355:A:G 2.571275e-02
## 398   16:57548744:T:C 2.584089e-02
## 520   2:128050134:T:C 2.586018e-02
## 118  10:121129797:G:A 2.586723e-02
## 32    1:170569689:G:A 2.591555e-02
## 156  11:111171709:C:A 2.595510e-02
## 250   12:64971147:G:A 2.613864e-02
## 796   5:107975671:A:G 2.618895e-02
## 621   20:34025756:A:G 2.620091e-02
## 169    11:1909006:T:C 2.623693e-02
## 693   3:185548683:G:A 2.628930e-02
## 952   7:116154015:T:G 2.630024e-02
## 710    3:53134098:T:C 2.637836e-02
## 620   20:33718706:G:A 2.667249e-02
## 997   8:117209548:A:G 2.667751e-02
## 117  10:120738290:C:T 2.670669e-02
## 280   13:33143406:G:A 2.672287e-02
## 646    20:8626271:C:A 2.684549e-02
## 727   4:103936001:G:A 2.700609e-02
## 731   4:107515773:A:G 2.710300e-02
## 442   17:64473021:A:G 2.726536e-02
## 859    5:81996389:A:G 2.735094e-02
## 488    19:1384690:G:A 2.736502e-02
## 683   3:172046933:A:G 2.737834e-02
## 789    4:82155568:C:A 2.738706e-02
## 915    6:35246903:G:A 2.745500e-02
## 279   13:33057345:G:T 2.755676e-02
## 241   12:51155663:C:T 2.762854e-02
## 732     4:1078124:G:A 2.769558e-02
## 131   10:32082658:T:C 2.788147e-02
## 35    1:176794066:G:A 2.795765e-02
## 974    7:23475919:G:A 2.802421e-02
## 317   14:54410919:T:C 2.806008e-02
## 767   4:181909685:G:A 2.832432e-02
## 1061  9:118169080:A:G 2.833457e-02
## 238    12:4368352:T:C 2.839215e-02
## 396   16:53842908:G:A 2.840857e-02
## 624   20:38552078:T:C 2.864745e-02
## 1018  8:141992778:T:C 2.865140e-02
## 1017  8:141729463:C:T 2.881498e-02
## 879   6:134173151:A:G 2.887869e-02
## 412    17:1054071:T:C 2.899031e-02
## 75     1:38386727:G:A 2.899863e-02
## 795   5:107566842:C:T 2.911059e-02
## 102  10:101805442:C:T 2.926966e-02
## 159  11:117283676:T:C 2.937196e-02
## 323   14:65567515:G:T 2.937272e-02
## 869   6:117490664:T:C 2.937322e-02
## 566   2:217935116:T:C 2.953341e-02
## 108  10:105324774:C:T 2.955258e-02
## 295   13:73957681:A:G 2.955401e-02
## 528   2:145645775:G:A 2.980645e-02
## 841    5:33989518:C:T 2.987911e-02
## 752   4:145565826:C:T 3.046719e-02
## 640   20:60958269:T:C 3.073592e-02
## 363   15:67446831:G:A 3.083248e-02
## 16    1:149938898:T:C 3.104603e-02
## 852    5:67599656:T:G 3.122608e-02
## 776    4:41261500:T:C 3.129284e-02
## 421   17:36101156:T:C 3.134972e-02
## 577   2:232322779:G:A 3.146841e-02
## 448   17:69923355:C:T 3.150043e-02
## 303   13:87001719:T:C 3.152685e-02
## 587    2:28635740:T:C 3.163465e-02
## 218  12:121416988:A:G 3.163669e-02
## 522   2:136187345:C:T 3.169046e-02
## 888   6:152437016:T:C 3.183736e-02
## 908    6:28747902:T:C 3.196243e-02
## 130   10:30323892:G:A 3.200226e-02
## 840    5:33499437:A:G 3.207782e-02
## 961   7:139942304:G:A 3.210554e-02
## 239    12:4942953:T:G 3.219421e-02
## 524   2:142746083:G:A 3.231244e-02
## 708     3:4742276:A:G 3.249171e-02
## 873   6:121781390:T:C 3.263183e-02
## 1031   8:36858483:A:G 3.279838e-02
## 668   3:134379752:C:T 3.281386e-02
## 909    6:31592820:C:T 3.294651e-02
## 145   10:80845746:G:A 3.320756e-02
## 231   12:26228883:G:A 3.352449e-02
## 509    19:5146582:A:G 3.355730e-02
## 1049  9:105754656:T:C 3.364954e-02
## 191   11:65583066:G:T 3.380290e-02
## 10    1:119491784:A:G 3.438420e-02
## 483   19:10803395:A:G 3.464492e-02
## 379   15:89851580:G:A 3.475654e-02
## 803   5:127696022:A:C 3.477973e-02
## 562   2:208526140:T:C 3.485917e-02
## 1028   8:27195121:T:C 3.499621e-02
## 76     1:38559352:A:G 3.500686e-02
## 892   6:160833664:C:T 3.506576e-02
## 829   5:172994624:G:A 3.535245e-02
## 319   14:55245149:A:G 3.542141e-02
## 889   6:156587831:T:C 3.559188e-02
## 867   6:116451442:A:G 3.602886e-02
## 222   12:13075379:G:A 3.609164e-02
## 688   3:183355405:A:G 3.614213e-02
## 707    3:46600520:A:G 3.646873e-02
## 729   4:106084778:C:T 3.651783e-02
## 20    1:154404406:A:C 3.670752e-02
## 730   4:106216205:T:C 3.713331e-02
## 706    3:41243742:T:G 3.730801e-02
## 1093   9:98265780:C:T 3.781516e-02
## 382   15:95312071:T:C 3.847165e-02
## 434   17:53209774:A:C 3.849144e-02
## 805     5:1297488:C:T 3.849828e-02
## 513   2:101576199:G:A 3.900736e-02
## 695   3:186004526:T:C 3.933196e-02
## 374   15:79121776:C:T 3.986323e-02
## 844     5:4012694:G:A 3.986729e-02
## 138    10:5886734:C:T 3.987780e-02
## 639   20:60920887:G:A 4.081819e-02
## 885   6:151942194:T:G 4.149024e-02
## 271   13:21570246:T:G 4.210229e-02
## 631   20:48316027:C:T 4.369981e-02
## 907    6:26200677:A:G 4.507931e-02
## 225   12:18040964:A:C 4.572318e-02
## 728   4:106061534:C:A 4.717647e-02
## 734   4:111586468:A:G 4.918761e-02
## 224   12:14520701:A:G 4.978408e-02
## 806   5:131585958:A:G 5.031346e-02
## 514    2:10178479:C:T 5.415964e-02
## 312   14:32981484:G:A 5.612800e-02
## 736   4:111706275:A:G 5.732161e-02
hist(temp.locus_cohort.maxdeltaaf$maxdeltaaaf)

# On the basis of this histogram, set a max delta aaf threshold of
# 4%.
mean(temp.locus_cohort.maxdeltaaf$maxdeltaaaf < 0.04)
## [1] 0.9881279
temp.sel_loci = temp.locus_cohort.maxdeltaaf$vid[temp.locus_cohort.maxdeltaaf$maxdeltaaaf < 0.04]
afs = afs[afs$vid %in% temp.sel_loci,]
models = models[models$vid %in% temp.sel_loci,]

Compare PRS distributions between cohorts

PRS calculation

scores = daply(expand.grid(cohort = cohorts.sel, model = models.sel), .(model, cohort), function(cm) 
    cohort_mean_score(afs.nomissing[afs.nomissing$cohort == cm$cohort,], models[models$id == cm$model,]), .progress = "text")

set.seed(314159)
boot_scores = daply(expand.grid(cohort = cohorts.sel, model = models.sel), .(model, cohort), function(cm) 
    cohort_mean_score_boot(afs.nomissing[afs.nomissing$cohort == cm$cohort,], models[models$id == cm$model,], B = 5000, drawmissing = TRUE), .progress = "text")
# Calculate all approximate tests, but obviously we will discard most
# of the outputs.
set.seed(314159)
boot_scores_tests = boot_tests(boot_scores, B = 100000)
saveRDS(list(
    scores = scores,
    boot = boot_scores,
    tests = boot_scores_tests), file = "common_variants.rds")

PRS distributions

library(gplots)
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
library(viridis)
## Loading required package: viridisLite
heatmap.2(scores, trace = "none", scale = "row", dendrogram = "none", Colv = FALSE, Rowv = FALSE, col = viridis(100), margin = c(7, 25))

library(ggplot2)
ggplot(adply(boot_scores, c(1,2), function(x) data.frame(boot_score = x)), aes(x = boot_score, col = cohort)) + stat_ecdf() + facet_wrap(~ model, scales = "free") + theme_bw()

PRS tests

Examine relevant test p-values.

target_tests = list(
    list(model = "AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143",             cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "DiastolicBP:Warren:10.1038/ng.3768",                      cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "EOCAD:Theriault:10.1161/circgen.117.001849",              cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "PulsePressure:Warren:10.1038/ng.3768",                    cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "SystolicBP:Warren:10.1038/ng.3768",                       cohort1 = "gnomad",        cohort2 = "mgrborig" ),

    list(model = "AlzheimersDisease:Lambert:10.1038/ng.2802",               cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "ShortLifespan:Deelen:10.1093/hmg/ddu139",                 cohort1 = "gnomad",        cohort2 = "mgrborig" ),

    list(model = "BreastCancer:Michailidou:10.1038/nature24284",            cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "ColorectalCancer:Schumacher:10.1038/ncomms8138",          cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "Melanoma:Law:10.1038/ng.3373",                            cohort1 = "gnomad",        cohort2 = "mgrborig" ),
    list(model = "ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315",    cohort1 = "gnomad",        cohort2 = "mgrborig" ),

    list(model = "AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143",             cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "DiastolicBP:Warren:10.1038/ng.3768",                      cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "EOCAD:Theriault:10.1161/circgen.117.001849",              cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "PulsePressure:Warren:10.1038/ng.3768",                    cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "SystolicBP:Warren:10.1038/ng.3768",                       cohort1 = "ukbb",          cohort2 = "mgrborig" ),

    list(model = "AlzheimersDisease:Lambert:10.1038/ng.2802",               cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "ShortLifespan:Deelen:10.1093/hmg/ddu139",                 cohort1 = "ukbb",          cohort2 = "mgrborig" ),

    list(model = "BreastCancer:Michailidou:10.1038/nature24284",            cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "ColorectalCancer:Schumacher:10.1038/ncomms8138",          cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "Melanoma:Law:10.1038/ng.3373",                            cohort1 = "ukbb",          cohort2 = "mgrborig" ),
    list(model = "ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315",    cohort1 = "ukbb",          cohort2 = "mgrborig" )
)
target_tests = ldply(target_tests, unlist)

boot_scores_tests.targeted = merge(target_tests, boot_scores_tests, all = FALSE)

stopifnot(nrow(boot_scores_tests.targeted) == nrow(target_tests))

boot_scores_tests.targeted$p.value.holm = p.adjust(boot_scores_tests.targeted$p.value, "holm")
boot_scores_tests.targeted$p.value.bh = p.adjust(boot_scores_tests.targeted$p.value, "BH")
boot_scores_tests.targeted$p.value.by = p.adjust(boot_scores_tests.targeted$p.value, "BY")
boot_scores_tests.targeted
##                                                   model cohort1  cohort2
## 1           AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143  gnomad mgrborig
## 2           AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143    ukbb mgrborig
## 3             AlzheimersDisease:Lambert:10.1038/ng.2802  gnomad mgrborig
## 4             AlzheimersDisease:Lambert:10.1038/ng.2802    ukbb mgrborig
## 5          BreastCancer:Michailidou:10.1038/nature24284  gnomad mgrborig
## 6          BreastCancer:Michailidou:10.1038/nature24284    ukbb mgrborig
## 7        ColorectalCancer:Schumacher:10.1038/ncomms8138  gnomad mgrborig
## 8        ColorectalCancer:Schumacher:10.1038/ncomms8138    ukbb mgrborig
## 9                    DiastolicBP:Warren:10.1038/ng.3768  gnomad mgrborig
## 10                   DiastolicBP:Warren:10.1038/ng.3768    ukbb mgrborig
## 11           EOCAD:Theriault:10.1161/circgen.117.001849  gnomad mgrborig
## 12           EOCAD:Theriault:10.1161/circgen.117.001849    ukbb mgrborig
## 13                         Melanoma:Law:10.1038/ng.3373  gnomad mgrborig
## 14                         Melanoma:Law:10.1038/ng.3373    ukbb mgrborig
## 15 ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315  gnomad mgrborig
## 16 ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315    ukbb mgrborig
## 17                 PulsePressure:Warren:10.1038/ng.3768  gnomad mgrborig
## 18                 PulsePressure:Warren:10.1038/ng.3768    ukbb mgrborig
## 19              ShortLifespan:Deelen:10.1093/hmg/ddu139  gnomad mgrborig
## 20              ShortLifespan:Deelen:10.1093/hmg/ddu139    ukbb mgrborig
## 21                    SystolicBP:Warren:10.1038/ng.3768  gnomad mgrborig
## 22                    SystolicBP:Warren:10.1038/ng.3768    ukbb mgrborig
##     delta.median     delta.lcl    delta.ucl      p.value p.value.holm
## 1   6.475078e-04  5.202614e-04 0.0007722520 0.0000099999 0.0002199978
## 2   1.598614e-04  4.860890e-05 0.0002661771 0.0037499625 0.0487495125
## 3   1.911866e-03 -4.309217e-04 0.0042158401 0.1097489025 0.7682423176
## 4   4.323625e-03  2.338296e-03 0.0063033699 0.0000099999 0.0002199978
## 5   1.648259e-04  5.671997e-05 0.0002743956 0.0028099719 0.0393396066
## 6   9.803052e-05  4.840047e-06 0.0001936397 0.0398696013 0.3690963090
## 7   8.438538e-05 -2.741711e-04 0.0004301698 0.6409235908 1.0000000000
## 8   2.723653e-04 -4.011726e-05 0.0005682770 0.0847491525 0.6779932201
## 9   4.293884e-03  3.232759e-03 0.0053713511 0.0000099999 0.0002199978
## 10  9.886771e-04  6.362128e-05 0.0019092678 0.0369096309 0.3690963090
## 11  6.620420e-04  4.886940e-04 0.0008394269 0.0000099999 0.0002199978
## 12  3.283545e-04  1.769171e-04 0.0004824692 0.0000099999 0.0002199978
## 13 -4.491547e-05 -9.021687e-04 0.0008170986 0.9185408146 1.0000000000
## 14  1.031671e-04 -6.348227e-04 0.0008461500 0.7836421636 1.0000000000
## 15  6.561792e-04  1.779959e-04 0.0011511427 0.0080699193 0.0887691123
## 16  7.237224e-04  3.154154e-04 0.0011578574 0.0005099949 0.0076499235
## 17 -5.880989e-04 -2.158566e-03 0.0009800814 0.4631853681 1.0000000000
## 18  9.828541e-04 -3.693966e-04 0.0023254061 0.1543084569 0.7715422846
## 19  2.151215e-03  6.477163e-04 0.0036330782 0.0049899501 0.0598794012
## 20  2.667790e-03  1.397707e-03 0.0039380330 0.0000099999 0.0002199978
## 21  2.071831e-03 -5.498196e-04 0.0047020101 0.1200487995 0.7682423176
## 22  4.440835e-03  2.158787e-03 0.0067291764 0.0000099999 0.0002199978
##      p.value.bh   p.value.by
## 1  3.142826e-05 0.0001159958
## 2  8.249918e-03 0.0304489048
## 3  1.509047e-01 0.5569612174
## 4  3.142826e-05 0.0001159958
## 5  6.868820e-03 0.0253515326
## 6  6.265223e-02 0.2312376829
## 7  7.050159e-01 1.0000000000
## 8  1.242988e-01 0.4587634994
## 9  3.142826e-05 0.0001159958
## 10 6.246245e-02 0.2305372466
## 11 3.142826e-05 0.0001159958
## 12 3.142826e-05 0.0001159958
## 13 9.185408e-01 1.0000000000
## 14 8.209585e-01 1.0000000000
## 15 1.479485e-02 0.0546050360
## 16 1.402486e-03 0.0051763138
## 17 5.363199e-01 1.0000000000
## 18 1.885992e-01 0.6960845190
## 19 9.979900e-03 0.0368339479
## 20 3.142826e-05 0.0001159958
## 21 1.553573e-01 0.5733946705
## 22 3.142826e-05 0.0001159958
boot_scores_tests.targeted[boot_scores_tests.targeted$p.value.bh < 0.05,]
##                                                   model cohort1  cohort2
## 1           AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143  gnomad mgrborig
## 2           AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143    ukbb mgrborig
## 4             AlzheimersDisease:Lambert:10.1038/ng.2802    ukbb mgrborig
## 5          BreastCancer:Michailidou:10.1038/nature24284  gnomad mgrborig
## 9                    DiastolicBP:Warren:10.1038/ng.3768  gnomad mgrborig
## 11           EOCAD:Theriault:10.1161/circgen.117.001849  gnomad mgrborig
## 12           EOCAD:Theriault:10.1161/circgen.117.001849    ukbb mgrborig
## 15 ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315  gnomad mgrborig
## 16 ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315    ukbb mgrborig
## 19              ShortLifespan:Deelen:10.1093/hmg/ddu139  gnomad mgrborig
## 20              ShortLifespan:Deelen:10.1093/hmg/ddu139    ukbb mgrborig
## 22                    SystolicBP:Warren:10.1038/ng.3768    ukbb mgrborig
##    delta.median    delta.lcl    delta.ucl      p.value p.value.holm
## 1  0.0006475078 5.202614e-04 0.0007722520 0.0000099999 0.0002199978
## 2  0.0001598614 4.860890e-05 0.0002661771 0.0037499625 0.0487495125
## 4  0.0043236250 2.338296e-03 0.0063033699 0.0000099999 0.0002199978
## 5  0.0001648259 5.671997e-05 0.0002743956 0.0028099719 0.0393396066
## 9  0.0042938844 3.232759e-03 0.0053713511 0.0000099999 0.0002199978
## 11 0.0006620420 4.886940e-04 0.0008394269 0.0000099999 0.0002199978
## 12 0.0003283545 1.769171e-04 0.0004824692 0.0000099999 0.0002199978
## 15 0.0006561792 1.779959e-04 0.0011511427 0.0080699193 0.0887691123
## 16 0.0007237224 3.154154e-04 0.0011578574 0.0005099949 0.0076499235
## 19 0.0021512152 6.477163e-04 0.0036330782 0.0049899501 0.0598794012
## 20 0.0026677904 1.397707e-03 0.0039380330 0.0000099999 0.0002199978
## 22 0.0044408354 2.158787e-03 0.0067291764 0.0000099999 0.0002199978
##      p.value.bh   p.value.by
## 1  3.142826e-05 0.0001159958
## 2  8.249918e-03 0.0304489048
## 4  3.142826e-05 0.0001159958
## 5  6.868820e-03 0.0253515326
## 9  3.142826e-05 0.0001159958
## 11 3.142826e-05 0.0001159958
## 12 3.142826e-05 0.0001159958
## 15 1.479485e-02 0.0546050360
## 16 1.402486e-03 0.0051763138
## 19 9.979900e-03 0.0368339479
## 20 3.142826e-05 0.0001159958
## 22 3.142826e-05 0.0001159958

Investigate basis of significant PRS differences

temp = boot_scores_tests.targeted[boot_scores_tests.targeted$p.value.bh < 0.05,]
for (i in 1:nrow(temp))
{
    dafplot(
        afs1 = afs.nomissing[afs.nomissing$cohort == temp$cohort1[i],], 
        afs2 = afs.nomissing[afs.nomissing$cohort == temp$cohort2[i],], 
        model = models[models$id == temp$model[i],],
        main = temp$model[i],
        xlab = "",
        # xlab = expression(paste("PRS ", beta)),
        ylab = sprintf("MAF %s - %s", temp$cohort1[i], temp$cohort2[i]),
        sub = sprintf("delta_median=%.3e  (%.3e-%.3e)\np.raw=%.4f  p.holm=%.4f  p.bh=%.4f", temp$delta.median[i], temp$delta.lcl[i], temp$delta.ucl[i], temp$p.value[i], temp$p.value.holm[i], temp$p.value.bh[i]))
}

library(reshape2)
boot_scores.summary = adply(boot_scores, c(1,2), function(x) data.frame(median = median(x), lcl = quantile(x, 0.025), ucl = quantile(x, 0.975)))
boot_scores.summary = merge(boot_scores.summary, melt(scores, value.name = "score"))
temp.mgrb_norm = boot_scores.summary[boot_scores.summary$cohort == "mgrborig", c("model", "median", "lcl", "ucl")]
temp.mgrb_norm$width = temp.mgrb_norm$ucl - temp.mgrb_norm$lcl
# Scale so that MGRB's [lcl, ucl] --> [-0.5, 0.5]
temp.mgrb_norm$a1 = 1/temp.mgrb_norm$width
temp.mgrb_norm$a0 = -0.5 - temp.mgrb_norm$lcl*temp.mgrb_norm$a1
temp.mgrb_norm = temp.mgrb_norm[,c("model", "a0", "a1")]
boot_scores.summary = merge(boot_scores.summary, temp.mgrb_norm)
boot_scores.summary$score.norm = boot_scores.summary$score*boot_scores.summary$a1 + boot_scores.summary$a0
boot_scores.summary$lcl.norm = boot_scores.summary$lcl*boot_scores.summary$a1 + boot_scores.summary$a0
boot_scores.summary$ucl.norm = boot_scores.summary$ucl*boot_scores.summary$a1 + boot_scores.summary$a0
ggplot(boot_scores.summary[boot_scores.summary$cohort %in% c("ukbb", "ukbb_75_inf", "ukbb_70_75", "ukbb_65_70", "ukbb_60_65", "ukbb_55_60", "ukbb_0_55", "gnomad", "mgrborig"),], aes(x = cohort, y = score, ymin = lcl, ymax = ucl)) + geom_point() + geom_errorbar(width = 0.5) + theme_bw() + facet_wrap(~ model, scales = "free") + coord_flip()

ggplot(boot_scores.summary[boot_scores.summary$cohort %in% c("ukbb", "ukbb_75_inf", "ukbb_0_55", "gnomad"),], aes(x = paste(model, cohort), y = score.norm, ymin = lcl.norm, ymax = ucl.norm, col = cohort)) + geom_point() + geom_rect(aes(ymin = -0.5, ymax = 0.5, xmin = -Inf, xmax = Inf), data = boot_scores.summary[boot_scores.summary$cohort %in% c("ukbb", "ukbb_75_inf", "ukbb_0_55", "gnomad"),][1,], fill = rgb(0, 0, 0, 0.1)) + geom_point() + geom_errorbar(width = 0.5) + theme_bw() + coord_flip()

ggplot(boot_scores.summary[boot_scores.summary$cohort %in% c("ukbb", "gnomad", "mgrborig"),], aes(x = cohort, y = score, ymin = lcl, ymax = ucl)) + geom_point() + geom_errorbar(width = 0.5) + theme_bw() + facet_wrap(~ model, scales = "free") + coord_flip()

ggplot(boot_scores.summary[boot_scores.summary$cohort %in% c("ukbb", "gnomad"),], aes(x = paste(model, cohort), y = score.norm, ymin = lcl.norm, ymax = ucl.norm, col = cohort)) + geom_point() + geom_rect(aes(ymin = -0.5, ymax = 0.5, xmin = -Inf, xmax = Inf), data = boot_scores.summary[boot_scores.summary$cohort %in% c("ukbb", "gnomad"),][1,], fill = rgb(0, 0, 0, 0.1)) + geom_point() + geom_errorbar(width = 0.5) + theme_bw() + coord_flip()

sessionInfo()
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
## 
## Matrix products: default
## BLAS: /home/markp/.prefix/lib/R/lib/libRblas.so
## LAPACK: /home/markp/.prefix/lib/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_3.0.0     viridis_0.5.1     viridisLite_0.3.0 gplots_3.0.1     
## [5] plyr_1.8.4        reshape2_1.4.3    rmarkdown_1.10   
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.17       knitr_1.20         magrittr_1.5      
##  [4] munsell_0.5.0      colorspace_1.3-2   rlang_0.2.1       
##  [7] stringr_1.3.1      caTools_1.17.1.1   tools_3.5.0       
## [10] grid_3.5.0         packrat_0.4.9-3    gtable_0.2.0      
## [13] KernSmooth_2.23-15 withr_2.1.2        htmltools_0.3.6   
## [16] gtools_3.8.1       lazyeval_0.2.1     yaml_2.1.19       
## [19] rprojroot_1.3-2    digest_0.6.15      tibble_1.4.2      
## [22] gridExtra_2.3      bitops_1.0-6       evaluate_0.10.1   
## [25] labeling_0.3       gdata_2.18.0       stringi_1.2.3     
## [28] pillar_1.2.3       compiler_3.5.0     scales_0.5.0      
## [31] backports_1.1.2